Applying Synthetic Minority Over-sampling Technique and Support Vector Machine to Develop a Classifier for Parkinson’s disease

Abstract

As the number of Parkinson’s disease patients increases in the elderly population, it has become a critical issue to understand the early characteristics of Parkinson’s disease and to detect Parkinson’s disease as soon as possible during normal aging. This study minimized the imbalance issue by employing Synthetic Minority Over-sampling Technique (SMOTE), developed eight Support Vector Machine (SVM) models for predicting Parkinson’s disease using different kernel types {(C-SVM or Nu-SVM)×(Gaussian kernel, linear, polynomial, or sigmoid algorithm)}, and compared the accuracy, sensitivity, and specificity of the developed models. This study evaluated 76 senior citizens with Parkinson’s disease (32 males and 44 females) and 285 healthy senior citizens without Parkinson’s disease (148 males and 137 females). The analysis results showed that the liner kernel-based Nu-SVM had the highest sensitivity (62.0%), specificity (81.6%), and overall accuracy (71.3%). The major negative relationship factors of the Parkinson’s disease prediction model were MMSE-K, Stroop Test, Rey Complex Figure Test (RCFT), verbal memory test, ADL, IADL, 70 years old or older, middle school graduation or below, and women. When the influence of variables was compared using “functional weight”, RCFT was identified as the most influential variable in the model for distinguishing Parkinson’s disease from healthy elderly. The results of this study implied that developing a prediction model by using linear kernel-based Nu-SVM would be more accurate than other kernel-based SVM models for handling imbalanced disease data.